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On the algorithmic effectiveness of digraph decompositions and complexity measures

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dc.contributor.author Lampis, M en
dc.contributor.author Kaouri, G en
dc.contributor.author Mitsou, V en
dc.date.accessioned 2014-03-01T02:45:42Z
dc.date.available 2014-03-01T02:45:42Z
dc.date.issued 2008 en
dc.identifier.issn 03029743 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/32332
dc.subject Digraph decompositions en
dc.subject Parameterized complexity en
dc.subject Treewidth en
dc.subject.other Complexity measures en
dc.subject.other Digraph decompositions en
dc.subject.other Graph problems en
dc.subject.other Hamiltonian en
dc.subject.other NP-hard en
dc.subject.other Parameterized complexity en
dc.subject.other Polynomial algorithms en
dc.subject.other Treewidth en
dc.subject.other Decision trees en
dc.subject.other Graph theory en
dc.subject.other Hamiltonians en
dc.subject.other Trees (mathematics) en
dc.subject.other Parallel processing systems en
dc.title On the algorithmic effectiveness of digraph decompositions and complexity measures en
heal.type conferenceItem en
heal.identifier.primary 10.1007/978-3-540-92182-0_22 en
heal.identifier.secondary http://dx.doi.org/10.1007/978-3-540-92182-0_22 en
heal.publicationDate 2008 en
heal.abstract We place our focus on the gap between treewidth's success in producing fixed-parameter polynomial algorithms for hard graph problems, and specifically Hamiltonian Circuit and Max Cut, and the failure of its directed variants (directed tree-width [9], DAG-width [11] and kelly-width [8]) to replicate it in the realm of digraphs. We answer the question of why this gap exists by giving two hardness results: we show that Directed Hamiltonian Circuit is W[2]-hard when the parameter is the width of the input graph, for any of these widths, and that Max Di Cut remains NP-hard even when restricted to DAGs, which have the minimum possible width under all these definitions. Our results also apply to directed pathwidth. © 2008 Springer Berlin Heidelberg. en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.identifier.doi 10.1007/978-3-540-92182-0_22 en
dc.identifier.volume 5369 LNCS en
dc.identifier.spage 220 en
dc.identifier.epage 231 en


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